Environmental degradation, climate variability, and urbanization have intensified the need for intelligent and scalable environmental monitoring systems. Conventional methods of data collection often suffer from high costs, limited spatial coverage, and delayed reporting, thereby limiting their effectiveness for real-time decision-making. This paper presents an Internet of Things (IoT)-driven environmental monitoring model that integrates ThingsBoard API and the Message Queuing Telemetry Transport (MQTT) protocol to provide a cost-effective, secure, and scalable solution for real-time data acquisition, transmission, and visualization. The proposed model employs a layered architecture consisting of sensor nodes, an MQTT communication layer, and an application layer powered by ThingsBoard. Sensor nodes equipped with air quality, temperature, humidity, and water quality sensors collect environmental parameters and publish data through an MQTT broker. ThingsBoard API ingests and processes the telemetry data, enabling seamless integration with dashboards, rules engines, and external systems. The system supports role-based access control, encrypted communication, and token-based device authentication, thereby addressing security and privacy concerns. Key features of the model include real-time monitoring, historical data analysis, customizable dashboards, and threshold-based alert mechanisms. Use cases span across urban air quality management, precision agriculture, water resource monitoring, climate research, and disaster risk reduction. The framework demonstrates high scalability, allowing seamless expansion to large numbers of devices and geographically distributed deployments. This highlight both the benefits and challenges of the model. While it significantly enhances environmental data availability and policy-making through digital transformation, challenges such as sensor calibration, connectivity limitations, and long-term maintenance remain. Future directions include integrating artificial intelligence for predictive analytics, deploying edge computing for latency reduction, and leveraging blockchain for data integrity. Overall, the IoT-driven model offers a robust pathway toward sustainable, data-driven environmental management and smart city development.